Voice identification method and apparatus

ABSTRACT

Embodiments of the present invention provide a voice identification method, which includes: obtaining voice data; obtaining a confidence value according to the voice data; obtaining a noise scenario according to the voice data; obtaining a confidence threshold corresponding to the noise scenario; and if the confidence value is greater than or equal to the confidence threshold, processing the voice data. An apparatus is also provided. The method and apparatus that flexibly adjust the confidence threshold according to the noise scenario greatly improve a voice identification rate under a noise environment.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No.201310027559.9, filed on Jan. 24, 2013, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

Embodiments of the present invention relate to the field of voiceprocessing technologies, and in particular, to a voice identificationmethod and apparatus.

BACKGROUND

A user generally performs voice identification by using voice assistantsoftware on a terminal device such as a mobile phone. A process ofperforming voice identification by using software such as a voiceassistant is that the user starts the voice assistant software to obtainvoice data; the voice data is sent to a noise reduction module for noisereduction processing; the voice data after the noise reductionprocessing is sent to a voice identification engine; the voiceidentification engine returns an identification result to the voiceassistant; and in order to reduce mis-determination, the voice assistantdetermines correctness of the identification result according to aconfidence threshold, and then displays the identification result.

Currently, a use effect of software such as a voice assistant isgenerally better in a quiet environment such as an office. However, theuse effect is poor in a noise environment (for example, an on-boardenvironment). In the industry, a voice identification rate is generallyimproved by using a software noise reduction method, but an improvementeffect is not distinct and sometimes the identification rate is evenlowered.

SUMMARY

The technical solutions provide a voice identification method andapparatus, which are used to improve a voice identification rate and atthe same time improve user experience.

In a first aspect, a voice identification method is provided andincludes: obtaining voice data; obtaining a confidence value accordingto the voice data; obtaining a noise scenario according to the voicedata; obtaining a confidence threshold corresponding to the noisescenario; and if the confidence value is greater than or equal to theconfidence threshold, processing the voice data.

With reference to the first aspect, in a first possible implementationmanner of the first aspect, the noise scenario specifically includes: anoise type and a noise magnitude.

With reference to the first possible implementation manner of the firstaspect, in a second possible implementation manner of the first aspect,the noise scenario includes the noise type, and the obtaining a noisescenario according to the voice data specifically includes: obtaining,according to the voice data, a frequency cepstrum coefficient of a noisein the voice data; and obtaining, according to the frequency cepstrumcoefficient of the noise and a pre-established noise type model, thenoise type of the voice data.

With reference to the second possible implementation manner of the firstaspect, in a third possible implementation manner of the first aspect, amethod for establishing a noise type model specifically includes:obtaining noise data; obtaining a frequency cepstrum coefficient of thenoise data according to the noise data; and processing the frequencycepstrum coefficient according to an EM algorithm, and establishing thenoise type model.

With reference to the third possible implementation manner of the firstaspect or the second possible implementation manner of the first aspect,in a fourth possible implementation manner of the first aspect, thenoise type model is a Gaussian mixture model.

With reference to the first possible implementation manner of the firstaspect, in a fifth possible implementation manner of the first aspect,the noise scenario includes a noise magnitude, and the obtaining a noisescenario according to the voice data specifically includes: obtaining,according to the voice data, a feature parameter of the voice data;performing voice activity detection according to the feature parameter;and obtaining the noise magnitude according to a result of the voiceactivity detection.

With reference to the first possible implementation manner of the firstaspect or the second possible implementation manner of the first aspector the third possible implementation manner of the first aspect or thefourth possible implementation manner of the first aspect or the fifthpossible implementation manner of the first aspect, in a sixth possibleimplementation manner of the first aspect, the noise magnitudespecifically includes: a signal-to-noise ratio; and a noise energylevel.

With reference to the first aspect or the first possible implementationmanner of the first aspect or the second possible implementation mannerof the first aspect or the third possible implementation manner of thefirst aspect or the fourth possible implementation manner of the firstaspect or the fifth possible implementation manner of the first aspector the sixth possible implementation manner of the first aspect, in aseventh possible implementation manner of the first aspect, theobtaining a confidence threshold corresponding to the noise scenariospecifically includes: obtaining, according to correspondence betweenpre-stored empirical data of a confidence threshold and the noisescenario, the confidence threshold corresponding to the noise scenario.

With reference to the first aspect or the first possible implementationmanner of the first aspect or the second possible implementation mannerof the first aspect or the third possible implementation manner of thefirst aspect or the fourth possible implementation manner of the firstaspect or the fifth possible implementation manner of the first aspector the sixth possible implementation manner of the first aspect or theseventh possible implementation manner of the first aspect, in an eighthpossible implementation manner of the first aspect, a user is promptedif the confidence value is smaller than the confidence threshold.

In a second aspect, a voice identification apparatus is provided andincludes: an obtaining unit, configured to obtain voice data; aconfidence value unit, configured to receive the voice data obtained bythe obtaining unit, and obtain a confidence value according to the voicedata; a noise scenario unit, configured to receive the voice dataobtained by the obtaining unit, and obtain a noise scenario according tothe voice data; a confidence threshold unit, configured to receive thenoise scenario of the noise scenario unit, and obtain a confidencethreshold corresponding to the noise scenario; and a processing unit,configured to receive the confidence value obtained by the confidencevalue unit and the confidence threshold obtained by the confidencethreshold unit, and if the confidence value is greater than or equal tothe confidence threshold, process the voice data.

With reference to the second aspect, in a first possible implementationmanner of the second aspect, the apparatus further includes: a modelestablishing unit, configured to obtain noise data, obtain a frequencycepstrum coefficient of the noise data according to the noise data,process the frequency cepstrum coefficient according to an EM algorithm,and establish a noise type model.

With reference to the first possible implementation manner of the secondaspect, in a second possible implementation manner of the second aspect,the noise scenario unit specifically includes: a noise type unit,configured to obtain, according to the voice data of the obtaining unit,a frequency cepstrum coefficient of a noise in the voice data, andobtain, according to the frequency cepstrum coefficient of the noise andthe noise type model of the model establishing unit, a noise type of thevoice data.

With reference to the second aspect or the first possible implementationmanner of the second aspect or the second possible implementation mannerof the second aspect, in a third possible implementation manner of thesecond aspect, the noise scenario unit further includes: a noisemagnitude unit, configured to obtain, according to the voice data of theobtaining unit, a feature parameter of the voice data, perform voiceactivity detection according to the feature parameter, and obtain anoise magnitude according to a result of the voice activity detection.

With reference to the second aspect or the first possible implementationmanner of the second aspect or the second possible implementation mannerof the second aspect or the third possible implementation manner of thesecond aspect, in a fourth possible implementation manner of the secondaspect, the apparatus further includes: a storage unit, configured tostore empirical data of a confidence threshold.

With reference to the fourth possible implementation manner of thesecond aspect, in a fifth possible implementation manner of the secondaspect, the confidence threshold unit is specifically configured toobtain, according to correspondence between the empirical data of theconfidence threshold pre-stored by the storage unit and the noisescenario, the confidence threshold corresponding to the noise scenario.

In a third aspect, a mobile terminal is provided and includes aprocessor and a microphone, where the microphone is configured to obtainvoice data; and the processor is configured to obtain a confidence valueand a noise scenario according to the voice data, obtain, according tothe noise scenario, a confidence threshold corresponding to the noisescenario, and if the confidence value is greater than or equal to theconfidence threshold, process the voice data.

With reference to the third aspect, in a first possible implementationmanner of the second aspect, the mobile terminal further includes: amemory, configured to store empirical data of a confidence threshold.

With reference to the first possible implementation manner of the thirdaspect, in a second possible implementation manner of the third aspect,the processor is specifically configured to obtain the confidence valueand the noise scenario according to the voice data; obtain, according tocorrespondence between the empirical data of the confidence thresholdpre-stored by the memory and the noise scenario, the confidencethreshold corresponding to the noise scenario; and if the confidencevalue is greater than or equal to the confidence threshold, process thevoice data.

The technical solutions of the present invention provide a voiceidentification method and apparatus. In the method and apparatus, thenoise scenario is obtained, and the confidence threshold is obtainedaccording to the pre-stored empirical data of the confidence thresholdand the noise scenario. The method and apparatus that flexibly adjustthe confidence threshold according to the noise scenario greatly improvea voice identification rate under a noise environment.

BRIEF DESCRIPTION OF DRAWINGS

To describe the technical solutions in the embodiments of the presentinvention more clearly, the following briefly introduces accompanyingdrawings required for describing the embodiments. Apparently, theaccompanying drawings in the following description show merely someembodiments of the present invention, and a person of ordinary skill inthe art may still derive other drawings according to these accompanyingdrawings without creative efforts.

FIG. 1 is a flowchart of a voice identification method according toEmbodiment 1 of the present invention;

FIG. 2 is a flowchart of another implementation manner of a voiceidentification method according to Embodiment 1 of the presentinvention;

FIG. 3 is a flowchart of another implementation manner of a voiceidentification method according to Embodiment 2 of the presentinvention;

FIG. 4 is a flowchart of another implementation manner of a voiceidentification method according to Embodiment 3 of the presentinvention;

FIG. 5 is a schematic structural diagram of a voice identificationapparatus according to Embodiment 4 of the present invention;

FIG. 6 is another possible schematic structural diagram of a voiceidentification apparatus according to Embodiment 4 of the presentinvention;

FIG. 7 is another possible schematic structural diagram of a voiceidentification apparatus according to Embodiment 4 of the presentinvention;

FIG. 8 is another possible schematic structural diagram of a voiceidentification apparatus according to Embodiment 4 of the presentinvention;

FIG. 9 is a schematic structural diagram of a mobile terminal accordingto Embodiment 5 of the present invention;

FIG. 10 is another possible schematic structural diagram of a mobileterminal according to Embodiment 5 of the present invention; and

FIG. 11 is a schematic structural diagram of a mobile phone according toan embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

To make the objectives, technical solutions, and advantages of theembodiments of the present invention clearer, the following clearlydescribes the technical solutions in the embodiments of the presentinvention with reference to the accompanying drawings in the embodimentsof the present invention. Apparently, the described embodiments aremerely a part rather than all of the embodiments of the presentinvention. All other embodiments obtained by a person of ordinary skillin the art based on the embodiments of the present invention withoutcreative efforts shall fall within the protection scope of theembodiments of the present invention.

Terms used in embodiments of the present invention are merely intendedto describe specific embodiments, but not to limit the presentinvention. “A” and “the” in a singular form used in the embodiments ofthe present invention and the claims also aim to include a plural form,except that the context clearly represents other meanings It should alsobe understood that the term “and/or” used in the specification refers toany or all possible combinations of one or more associated listed items.It should be further understood that the term “include” adopted in thespecification specifies existence of features, integers, steps,operations, elements and/or components, but does not exclude existenceor addition of other features, integers, steps, operations, components,elements, and their combinations.

In the embodiments of the present invention, the apparatus includes butis not limited to a device, such as a mobile phone, a personal digitalassistant (Personal Digital Assistant, PDA), a tablet computer, aportable device (for example, a portable computer), an on-board device,and an ATM machine (Automatic Teller Machine, automatic teller machine),to which it is not limited in the embodiments of the present invention.

Embodiment 1

FIG. 1 is a flowchart of a voice identification method according toEmbodiment 1 of the present invention.

As shown in FIG. 1, Embodiment 1 of the present invention provides avoice identification method, which may specifically include:

S100: Obtain voice data.

A user starts voice identification software, such as a voice assistant,on an apparatus to obtain, through a microphone, voice data that isinput by the user. It should be understood that the voice data may alsonot be input by the user, may also be input by a machine, and mayinclude any data including information.

S101: Obtain a confidence value according to the voice data.

Confidence refers to a degree of belief on authenticity of a specificthesis by a specific individual, and in the embodiment of the presentinvention, is the degree of belief on authenticity of an identificationresult of the voice data by the apparatus and so on. That is, theconfidence value is a numerical value used to indicate a credibilitydegree of a voice identification result. For example, the voice datainput by the user is “Give Zhang San calling, during a voice dataidentification process, a returned confidence value includes: a sentenceconfidence N1 (overall confidence of “Give Zhang San calling”), apreposed command word confidence N2 (“give” is a preposed command word,that is, the confidence value of “give” is N2), a person name confidenceN3 (“Zhang San” is a name, that is, the confidence value of “Zhang San”is N3), and a postposed command word confidence N4 (“calling” is apostposed command word, that is, the confidence of “calling” is N4).Usually, the sentence confidence N1 is obtained by integrating N2, N3,and N4. In a certain experiment, it is obtained, by testing, that theconfidence value of the voice data “Give Zhang San a call” input by theuser is N1=62, N2=50, N3=48, and N4=80, respectively.

It should be understood that step S102 may be before step S103, stepS102 may be after step S103, or step S102 and step S103 may be executedat the same time, to which it is not limited in the embodiment of thepresent invention.

S102: Obtain a noise scenario according to the voice data.

According to the voice data input by the user, the noise scenario isobtained. The noise scenario is a noise state when the user inputs thevoice data. That is, it may be understood as whether the user inputs thevoice data in a noise environment on a road, in a noise environment inan office, or in an on-board noise environment, and whether noise in acorresponding environment where the user is located is large or small.

It should be understood that step S102 may be before step S101, stepS102 may also be after step S101, or step S102 and step S101 may beexecuted at the same time, to which it is not limited in the embodimentof the present invention.

S103: Obtain a confidence threshold corresponding to the noise scenario.

The confidence threshold is used as an index for evaluating whether theconfidence value may be accepted, if the confidence value is greaterthan the confidence threshold, it is considered that an identificationresult is correct, and if the confidence value is smaller than theconfidence threshold, it is considered that the identification result isincorrect and the result cannot be believed. After the noise scenario ofthe voice data is obtained, the confidence threshold corresponding tothe noise scenario may be obtained according to the noise scenario.

S104: Process the voice data if the confidence value is greater than orequal to the confidence threshold.

If the confidence value is greater than or equal to the confidencethreshold, it is considered that the identification result of the voicedata is correct, that is, to process corresponding voice data. Forexample, if the confidence value N3 obtained in step S101 is 48 and theconfidence threshold obtained in step S103 is 40, then the confidencevalue is greater than the confidence threshold and the identificationresult of the voice data is correct. Further, an example is given fordescription, when the voice data is voice data including a command word,such as “Give Zhang San a call”, “Send Zhang San a short message”, and“Open an application program”, the voice identification belongs tocommand word identification, and then the apparatus executes acorresponding command and an operation such as making a call, sending ashort message, and opening an application program, If the voice dataidentification belongs to text dictation, an identification result textis displayed. That is, if the confidence value is greater than or equalto the confidence threshold, the voice data is processed.

The technical solution of the present invention provides a voiceidentification method. In the method, the noise scenario is obtained,and the confidence threshold is obtained according to pre-storedempirical data of a confidence threshold and the noise scenario. Themethod that flexibly adjusts the confidence threshold according to thenoise scenario greatly improves a voice identification rate under anoise environment.

Optionally

FIG. 2 is a flowchart of another implementation manner of a voiceidentification method according to Embodiment 1 of the presentinvention.

As shown in FIG. 2, the method further includes:

S1041: Prompt the user if the confidence value is smaller than theconfidence threshold.

If the confidence value is smaller than the confidence threshold, it isconsidered that the identification result of the voice data isincorrect, and the user is prompted. For example, if the confidencevalue N3 obtained in step S101 is 48 and the confidence thresholdobtained in step S103 is 50, then the confidence value is smaller thanthe confidence threshold and the identification result of the voice datais incorrect. Further, an example is given for description, when thevoice data is “Give Zhang San a call”, the apparatus determines that theidentification result of the voice data is incorrect, and the systemprompts the user to speak again and/or notifies the user of a fault.That is, if the confidence value is smaller than the confidencethreshold, the user is prompted to re-input or correct the fault.

The technical solution of the present invention provides a voiceidentification method. In the method, the noise scenario is obtained,and the confidence threshold is obtained according to pre-storedempirical data of a confidence threshold and the noise scenario. Themethod that flexibly adjusts the confidence threshold according to thenoise scenario greatly improves a voice identification rate under anoise environment.

Embodiment 2

FIG. 3 is a flowchart of another implementation manner of a voiceidentification method according to Embodiment 2 of the presentinvention.

Embodiment 2 of the present invention is described on a basis ofEmbodiment 1 of the present invention. As shown in FIG. 3, in step S102of Embodiment 1, the noise scenario specifically includes: a noise type;and a noise magnitude.

The noise type refers to a noise environment where a user is locatedwhen inputting voice data. That is, it may be understood as whether theuser is in a noise environment on a road, in a noise environment in anoffice, or in an on-board noise environment.

The noise magnitude represents a magnitude of noise in the noiseenvironment where the user is located when inputting the voice data.Optionally, the noise magnitude includes: a signal-to-noise ratio and anoise energy level. The signal-to-noise ratio is a ratio of voice datapower to noise data power, and is usually represented by decibels.Generally, a higher signal-to-noise ratio indicates a smaller noise datapower, otherwise, vice versa. The noise energy level is used to reflecta magnitude of energy of noise data in the voice data of the user. Thesignal-to-noise ratio and the noise energy level are combined toindicate the noise magnitude.

The noise scenario includes the noise type, in step S102 of Embodiment1, the obtaining a noise scenario according to the voice dataspecifically includes:

S1021: Obtain, according to the voice data, a frequency cepstrumcoefficient of a noise in the voice data.

According to the voice data input by the user, a voice data frame and anoise data frame are determined through voice activity detection (Voiceactivity detection, VAD), and after the noise data frame is obtained, afrequency cepstrum coefficient of the noise data frame is obtained. Mel(mel) is a unit of subjective pitch, and Hz (hertz) is a unit ofobjective pitch. A Mel frequency is proposed based on an auditoryfeature of a human ear, and has non-linear correspondence with an Hzfrequency. A frequency cepstrum coefficient (Mel Frequency CepstrumCoefficient, MFCC) is a cepstrum coefficient on the Mel frequency, hasgood identification performance, and is widely applied to a field suchas voice identification, voiceprint recognition, and languageidentification.

S1022: Obtain, according to the frequency cepstrum coefficient of thenoise and a pre-established noise type model, the noise type of thevoice data.

The frequency cepstrum coefficient is respectively substituted into eachpre-established noise type model for calculation, and if a calculationresult value of a certain noise type model is a maximum, it isconsidered that the user is located in an environment of the noise typewhen inputting the voice data, that is, the noise type of the voice datais obtained.

The pre-established noise type model in step S1022 is a Gaussian mixturemodel.

Gaussian density function estimation is a parameterized model, whichincludes two types; namely, a single Gaussian model (SingleGaussianModel, SGM) and a Gaussian mixture model (Gaussian mixturemodel, GMM). A Gaussian model is a valid clustering model, which mayconsider, according to different Gaussian probability density functionparameters, each established Gaussian model as a type. When a sample xis input, a value is calculated by using the Gaussian probabilitydensity function, and then it is determined, through a threshold,whether the sample belongs to an established Gaussian model. The GMM hasmultiple models, of which dividing is finer, is applicable to dividingof a complex object, and is widely applied to establishment of a complexobject model. For example, in voice identification, the GMM is used forclassification and model establishment of different noise types.

In the embodiment of the present invention, a process of establishingthe GMM of a certain noise type may be: inputting multiple groups ofnoise data of a same type, repeatedly training the GMM model accordingto the noise data, and finally obtaining the GMM of the noise type.

The Gaussian mixture model may be expressed by the following formula:

p(x)=Σ_(i=1) ^(N)α_(i) N(x;μ _(i),Σ_(i)), where Σ_(i=1) ^(N)α_(i)=1

The Gaussian model N(x;μ,Σ) may be expressed by the following formula:

${N( {{x;\mu},\sum} )} = {\frac{1}{\sqrt{2\; \pi {\sum }}}{\exp \lbrack {{- \frac{1}{2}}( {x - \mu} )^{T}{\sum^{- 1}( {x - \mu} )}} \rbrack}}$

where, N is a degree of mixing of the GMM model, that is, being combinedby N Gaussian models, α_(i) is a weight of an i^(th) Gaussian model, μis an average value, and Σ is a covariance matrix. In theory, any shapein space may use a GMM model for model establishing. Because an outputof the Gaussian model is a decimal between 0 and 1, for ease ofcalculation, generally, a natural logarithm (ln) is obtained from aresult, so as to become a floating-point number smaller than 0.

A method for establishing the pre-established noise type model in stepS1022 includes:

obtaining noise data. Obtain multiple groups of same-type noise, forexample, noise data such as on-board noise, street noise, and officenoise, which are used to establish the GMM of the type of noise data,that is, a noise type model of the type of noise data. It should beunderstood that in the present invention, another type of noise data mayalso be obtained, and a corresponding noise type model is establishedfor each type of noise data, to which it is not limited in theembodiment of the present invention;

obtaining, according to the noise data, the frequency cepstrumcoefficient of the noise data. Extract the frequency cepstrumcoefficient of the noise from the noise data. Mel (mel) is a unit ofsubjective pitch, and Hz (hertz) is a unit of objective pitch. A Melfrequency is proposed based on an auditory feature of a human ear, andhas non-linear correspondence with an Hz frequency. A frequency cepstrumcoefficient (Mel Frequency Cepstrum Coefficient, MFCC) is a cepstrumcoefficient on the Mel frequency, has good identification performance,and is widely applied to a field such as voice identification,voiceprint recognition, and language identification; and

processing the frequency cepstrum coefficient according to an EMalgorithm, and establishing a noise type model. In statistics, the EMalgorithm (Expectation-maximization algorithm, expectation-maximizationalgorithm) is used to search for a maximum likelihood estimation of aparameter in a probability model depending on an unobservable latentvariable. In statistical calculation, the expectation-maximization (EM)algorithm searches for the maximum likelihood estimation or a maximumposteriori estimation of the parameter, where the GMM depends on theunobservable latent variable (Latent Variable).

In the EM algorithm, calculation is performed alternately in two steps.A first step is to calculate expectation (E). Estimate an expectationvalue of an unknown parameter, and give a current parameter estimation.A second step is to perform maximization (M). Re-estimate a distributionparameter, so as to maximize likelihood of data, and give an expectedestimation of an unknown variable. As a whole, a procedure of the EMalgorithm is as follows: 1. Initialize the distribution parameter; 2.Repeat until convergence. Simply speaking, the EM algorithm is that,assuming that two parameters, A and B, are known by estimation, and thetwo are both unknown in a starting state, if information of A is known,information of B may be obtained, and reversely, if information of B isknown, information of A may be obtained. It may be considered to firstendow A with a certain initial value, so as to obtain an estimated valueof B, and then from a current value of B, re-estimate a value of A. Theprocess continues until convergence. The EM algorithm performs themaximum likelihood estimation on the parameter from an incomplete dataset, and is a quite simple and practical learning algorithm. Byalternately using the two steps of E and M, the EM algorithm graduallymodifies the parameter of the model, so that a likelihood probability ofthe parameter and a training sample is gradually increased, and finallyends at a maximum point. Intuitively understanding, the EM algorithm mayalso be considered as a successive approximation algorithm: Theparameter of the model is not known in advance, a set of parameters maybe randomly selected or a certain initial parameter may be roughly givenin advance, a most possible state corresponding to a group of parametersis determined, a probability of a possible result of each trainingsample is calculated, and a parameter is corrected through a sample in acurrent state to re-estimate the parameter, and a state of the model isre-determined under the new parameter. In this way, through a pluralityof iteration, circulation is performed until a certain convergencecondition is satisfied, so as to make the parameter of the modelgradually approximate to a real parameter.

The obtained frequency cepstrum coefficient is substituted into the EMalgorithm for training, and through a training process, parameters suchas N, α_(i), μ, and Σ in the Gaussian mixture model are obtained, andaccording to the parameters and p(x)=Σ_(i=1) ^(N)α_(i)N(x;μ_(i),Σ_(i)),where Σ_(i=1) ^(N)α_(i)=1, the Gaussian mixture model is established,that is, the noise type model corresponding to the noise type isestablished. Meanwhile, x is a frequency cepstrum coefficient.

For example, in step S102 in Embodiment 1, the obtaining a noisescenario according to the voice data is specifically as follows:

Obtain the frequency cepstrum coefficient of the noise frame of thevoice data according to the voice data, where the frequency cepstrumcoefficient is x in the Gaussian mixture model p(x)=Σ_(i=1)^(N)α_(i)N(x;μ_(i),Σ_(i)). It is assumed that there are two noise typemodels, one is a noise type model of on-board noise obtained throughon-board noise training, and the other is a noise type model ofnon-on-board noise obtained through non-on-board type noise (which mayinclude office noise, street noise, supermarket noise, and so on)training Assume that the voice data input by the user has 10 noiseframes, respectively substitute the frequency cepstrum coefficient ofeach noise frame, that is, x, into two noise type models p(x)=Σ_(i=1)^(N)α_(i)N(x;μ_(i),Σ_(i)) (where parameters such as N, α_(i), μ, and Σare known) to obtain a calculation result, obtain a logarithm from thecalculation result, and then perform cumulative average. A final resultis shown as table 1:

TABLE 1 Cumulative Number of noise frames 1 2 3 4 5 6 7 8 8 10 averageNoise type model of −46.8 −46.6 −45.3 −43.8 −47.8 −50.7 −46.5 −47.7−46.7 −45.7 −46.8 non-on-board noise Noise type model of −43.0 −41.9−41.3 −39.7 −42.1 −47.7 −41.5 −39.6 −43.6 −38.7 −41.9 on-board noise

The final result displays that the calculation result value of the noisetype model of the on-board noise is greater than the calculation resultvalue of the noise type model of the non-on-board noise (that is,−41.9>−46.8), so that the noise type of current voice data is on-boardnoise.

The technical solution of the present invention provides a method forimproving a voice identification rate under a noise environment. In themethod, the noise scenario is obtained, and the confidence threshold isobtained according to pre-stored empirical data of a confidencethreshold and the noise scenario. The method that flexibly adjusts theconfidence threshold according to the noise scenario greatly improves avoice identification rate under a noise environment.

Optionally

As shown in FIG. 3, the noise scenario includes a noise magnitude, instep S102 of Embodiment 1, the obtaining a noise scenario according tothe voice data specifically includes:

S1023: Obtain, according to the voice data, a feature parameter of thevoice data.

The feature parameter of the voice data is extracted according to thevoice data, where the feature parameter includes: sub-band energy, afundamental tone, and a cyclic factor.

For the sub-band energy, according to different useful components indifferent bands of the voice data, a band of 0-8K is divided into Nsub-bands, and energy of each frame of voice of each sub-band isrespectively calculated. A formula for calculating the sub-band energyis:

${ener} = {\frac{1}{L}{\sum\limits_{i = 0}^{L - 1}( {{x\lbrack i\rbrack}^{\bigwedge}2} )}}$

where, L is a frame length, and a frame of voice data is x[0]x[1] tox[L−1].

The fundamental tone and the periodic factor reflect a periodicalcomponent in the voice. In the voice, the periodic component is verypoor in a mute segment and a voiceless segment, and the periodicity isvery good in a voiced segment. Based on this point, voice framedetection may be performed.

S1024: Perform voice activity detection according to the featureparameter.

According to the voice data input by the user, the voice data frame andthe noise data frame are determined through the voice activity detection(Voice activity detection, VAD), and the fundamental tone, the periodicfactor, and the sub-band energy are combined, so as to performdetermination on a voice frame and a mute frame.

In VAD determination, the voice frame and the noise frame is determinedmainly based on the following two elements:

1) the energy of the voice frame is higher than the energy of the noiseframe; and

2) a frame with a stronger periodicity is generally the voice frame.

S1025: Obtain the noise magnitude according to a result of the voiceactivity detection.

According to a VAD determination result, respectively calculate anaverage energy of the voice frame and the noise frame to obtain a voiceenergy level (speechLev) and a noise energy level (noiseLev), and thenobtain, by calculating, a signal-to-noise ratio (SNR). The formula is:

${noiseLev} = {10*\log \; 10( {1 + {\frac{1}{Ln}{\sum\limits_{i = 1}^{Ln}{{ener}\lbrack N_{i} \rbrack}}}} )}$${speechLev} = {10*\log \; 10( {1 + {\frac{1}{Ls}{\sum\limits_{j = 1}^{Ls}{{ener}\lbrack S_{j} \rbrack}}}} )}$SNR = speechLev − noiseLev

where, Ln and Ls respectively represent the total number of noise framesand the total number of voice frames, ener[Ni] represents the energy ofthe i^(th) noise frame, and ener[Sj] represents the energy of the j^(th)voice frame.

The technical solution of the present invention provides a method forimproving a voice identification rate under a noise environment. In themethod, the noise scenario is obtained, and the confidence threshold isobtained according to pre-stored empirical data of a confidencethreshold and the noise scenario. The method that flexibly adjusts theconfidence threshold according to the noise scenario greatly improves avoice identification rate under a noise environment.

Embodiment 3

FIG. 4 is a flowchart of another implementation manner of a voiceidentification method according to Embodiment 3 of the presentinvention.

This embodiment is described on a basis of Embodiment 1, as shown inFIG. 4, the method of step S103 of Embodiment 1 specifically includes:

S1031: Obtain, according to correspondence between pre-stored empiricaldata of a confidence threshold and a noise scenario, a confidencethreshold corresponding to the noise scenario.

After the noise scenario of voice data is obtained, the confidencethreshold corresponding to the noise scenario is obtained according tothe correspondence between the pre-stored empirical data of theconfidence threshold and the noise scenario. That is, the confidencethreshold is obtained according to a noise type in the noise scenario, anoise magnitude, and correspondence of the empirical data of theconfidence threshold obtained through great amount of emulationmeasurement. The noise type indicates a type of environment where a useris located when voice identification is performed, and the noisemagnitude indicates the noise magnitude of the type of environment wherethe user is located. A principle for obtaining the confidence thresholdis that: In combination with the noise type, when noise is larger, alower confidence threshold is selected; and in combination with thenoise type, when the noise is smaller, a larger confidence threshold isset. The specific empirical data of the confidence threshold is obtainedby statistics collecting in emulation measurement.

For example

the noise type is an on-board environment. When the noise is larger(that is, a noise level is smaller than −30 dB, and a signal-to-noiseratio is smaller than 10 dB), it is obtained by statistics collecting inemulation measurement that, in the noise scenario, the empirical data ofthe confidence threshold is 35-50. Therefore, in the noise scenario, theobtained confidence threshold is a certain value between 35 and 50.

The noise type is the on-board environment, when the noise is smaller(the noise level is greater than −30 dB and smaller than −40 dB, and thesignal-to-noise ratio is greater than 10 dB and smaller than 20 dB), itis obtained by statistics collecting in emulation measurement that, inthe noise scenario, the empirical data of the confidence threshold is40-55. Therefore, in the noise scenario, the obtained confidencethreshold is a certain value between 40 and 55.

The noise type is an office environment, when the noise is smaller (thenoise level is smaller than −40 dB, and the signal-to-noise ratio isgreater than 20 dB), it is obtained by statistics collecting inemulation measurement that, in the noise scenario, the empirical data ofthe confidence threshold is 45-60. Therefore, in the noise scenario, theobtained confidence threshold is a certain value between 45 and 60.

The technical solution of the present invention provides a method forimproving a voice identification rate under a noise environment. In themethod, the noise scenario is obtained, and the confidence threshold isobtained according to the pre-stored empirical data of the confidencethreshold and the noise scenario. The method that flexibly adjusts theconfidence threshold according to the noise scenario greatly improves avoice identification rate under a noise environment.

Embodiment 4

FIG. 5 is a schematic structural diagram of a voice identificationapparatus according to Embodiment 4 of the present invention.

As shown in FIG. 5, the apparatus includes:

an obtaining unit 300, configured to obtain voice data;

a confidence value unit 301, configured to receive the voice dataobtained by the obtaining unit 300, and obtain a confidence valueaccording to the voice data;

a noise scenario unit 302, configured to receive the voice data obtainedby the obtaining unit 300, and obtain a noise scenario according to thevoice data;

a confidence threshold unit 303, configured to receive the noisescenario of the noise scenario unit 302, and obtain a confidencethreshold corresponding to the noise scenario; and

a processing unit 304, configured to receive the confidence valueobtained by the confidence value unit 301 and the confidence thresholdobtained by the confidence threshold unit 303, and if the confidencevalue is greater than or equal to the confidence threshold, process thevoice data.

The obtaining unit 300 obtains the voice data; the confidence value unit301 receives the voice data obtained by the obtaining unit 300, andobtains the confidence value according to the voice data; the noisescenario unit 302 receives the voice data obtained by the obtaining unit300, and obtains the noise scenario according to the voice data, wherethe noise scenario includes a noise type, a signal-to-noise ratio, and anoise energy level; the confidence threshold unit 303 receives the noisescenario of the noise scenario unit 302, and obtains the confidencethreshold corresponding to the noise scenario; and the processing unit304 receives the confidence value obtained by the confidence value unit301 and the confidence threshold obtained by the confidence thresholdunit 303, and if the confidence value is greater than or equal to theconfidence threshold, processes the voice data.

The obtaining unit 300, the confidence value unit 301, the noisescenario unit 302, the confidence threshold unit 303, and the processingunit 304 may be configured to execute the method described in stepsS100, S101, S102, S103, and S104 in Embodiment 1. For specificdescription, reference is made to the description of the method inEmbodiment 1, which is not repeatedly described herein.

The technical solution of the present invention provides a voiceidentification apparatus. In the apparatus, the noise scenario isobtained, and the confidence threshold is obtained according topre-stored empirical data of a confidence threshold and the noisescenario. The apparatus that flexibly adjusts the confidence thresholdaccording to the noise scenario greatly improves a voice identificationrate under a noise environment.

Optionally

FIG. 6 is another possible schematic structural diagram of a voiceidentification apparatus according to Embodiment 4 of the presentinvention.

As shown in FIG. 6, the apparatus further includes:

a model establishing unit 305, configured to obtain noise data, obtain afrequency cepstrum coefficient of the noise data according to the noisedata, process the frequency cepstrum coefficient according to an EMalgorithm, and establish a noise type model.

The model establishing unit 305 may be configured to execute the methodfor pre-establishing the noise type model in step S1022 of Embodiment 2.For specific description, reference is made to the description of themethod in Embodiment 2, which is not repeatedly described herein.

The technical solution of the present invention provides a voiceidentification apparatus. In the apparatus, the noise scenario isobtained, and the confidence threshold is obtained according topre-stored empirical data of a confidence threshold and the noisescenario. The apparatus that flexibly adjusts the confidence thresholdaccording to the noise scenario greatly improves a voice identificationrate under a noise environment.

Optionally

FIG. 7 is another possible schematic structural diagram of a voiceidentification apparatus according to Embodiment 4 of the presentinvention.

As shown in FIG. 7, the noise scenario unit specifically includes:

a noise type unit 3021, configured to obtain, according to the voicedata of the obtaining unit, a frequency cepstrum coefficient of a noisein the voice data, and obtain, according to the frequency cepstrumcoefficient of the noise and the noise type model of the modelestablishing unit, a noise type of the voice data, where, the noise typeunit 3021 may be configured to execute the method described in stepsS1021 and S1022 of Embodiment 2. For specific description, reference ismade to the description of the method in Embodiment 2, which is notrepeatedly described herein; and a noise magnitude unit 3022, configuredto obtain, according to the voice data of the obtaining unit, a featureparameter of the voice data, perform voice activity detection accordingto the feature parameter, and obtain a noise magnitude according to aresult of the voice activity detection,

where, the noise magnitude unit 3022 may be configured to execute themethod described in steps S1023, S1024, and S1025 of Embodiment 2. Forspecific description, reference is made to the description of the methodin Embodiment 2, which is not repeatedly described herein.

The technical solution of the present invention provides a voiceidentification apparatus. In the apparatus, the noise scenario isobtained, and the confidence threshold is obtained according topre-stored empirical data of a confidence threshold and the noisescenario. The apparatus that flexibly adjusts the confidence thresholdaccording to the noise scenario greatly improves a voice identificationrate under a noise environment.

Optionally

FIG. 8 is another possible schematic structural diagram of a voiceidentification apparatus according to Embodiment 4 of the presentinvention.

As shown in FIG. 8, the apparatus further includes:

a storage unit 306, configured to store empirical data of a confidencethreshold.

The confidence threshold unit 303 is specifically configured to obtain,according to correspondence between the empirical data of the confidencethreshold pre-stored by the storage unit 306 and the noise scenario, theconfidence threshold corresponding to the noise scenario.

The confidence threshold unit 303 may be configured to execute themethod described in step S1031 of Embodiment 3. For specificdescription, reference is made to the description of the method inEmbodiment 3, which is not repeatedly described herein.

The technical solution of the present invention provides a voiceidentification apparatus. In the apparatus, the noise scenario isobtained, and the confidence threshold is obtained according to thepre-stored empirical data of the confidence threshold and the noisescenario. The apparatus that flexibly adjusts the confidence thresholdaccording to the noise scenario greatly improves a voice identificationrate under a noise environment.

Embodiment 5

FIG. 9 is a schematic structural diagram of a mobile terminal accordingto Embodiment 5 of the present invention.

As shown in FIG. 9, the mobile terminal includes a processor and amicrophone, where the microphone 501 is configured to obtain voice data;and the processor 502 is configured to obtain a confidence value and anoise scenario according to the voice data, obtain, according to thenoise scenario, a confidence threshold corresponding to the noisescenario, and if the confidence value is greater than or equal to theconfidence threshold, process the voice data.

The microphone 501 and the processor 502 may be configured to executethe method described in steps S100, S101, S102, S103, and S104 ofEmbodiment 1. For specific description, reference is made to thedescription of the method in Embodiment 1, which is not repeatedlydescribed herein.

The technical solution of the present invention provides a mobileterminal. In the mobile terminal, the noise scenario is obtained, andthe confidence threshold is obtained according to pre-stored empiricaldata of a confidence threshold and the noise scenario. The mobileterminal that flexibly adjusts the confidence threshold according to thenoise scenario greatly improves a voice identification rate under anoise environment.

Optionally

As shown in FIG. 10, the mobile terminal further includes: a memory 503,configured to store empirical data of a confidence threshold.

The processor is specifically configured to obtain the confidence valueand the noise scenario according to the voice data; obtain, according tocorrespondence between the empirical data of the confidence thresholdpre-stored by the memory and the noise scenario, the confidencethreshold corresponding to the noise scenario; and if the confidencevalue is greater than or equal to the confidence threshold, process thevoice data.

The foregoing structure may be configured to execute the method inEmbodiment 1, Embodiment 2, and Embodiment 3. For specific description,reference is made to the description of the method in Embodiment 1,Embodiment 2, and Embodiment 3, which is not repeatedly describedherein.

The technical solution of the present invention provides a mobileterminal. In the apparatus, the noise scenario is obtained, and theconfidence threshold is obtained according to the pre-stored empiricaldata of the confidence threshold and the noise scenario. The mobileterminal that flexibly adjusts the confidence threshold according to thenoise scenario greatly improves a voice identification rate under anoise environment.

Embodiment 6

As shown in FIG. 11, in this embodiment, a mobile phone is taken as anexample for specific description of the embodiment of the presentinvention. It should be understood that the mobile phone shown in thefigure is only an example of the mobile phone, and the mobile phone mayhave more or fewer components than what is shown in the figure, maycombine two or more components, or may have different componentconfigurations. Various components shown in the figure may beimplemented in hardware or software including one or more signalprocessing and/or dedicated integrated circuits, or a combination ofhardware and software.

FIG. 11 is a schematic structural diagram of a mobile phone according toan embodiment of the present invention. As shown in FIG. 11, the mobilephone includes: a touchscreen 41, a memory 42, a CPU 43, a powermanagement chip 44, an RF circuit 45, a peripheral interface 46, anaudio circuit 47, a microphone 48, and an I/O sub-system 49.

The touchscreen 41 is an input interface and an output interface betweenthe mobile phone and a user, and in addition to a function of obtainingtouch information and a control instruction of the user, the touchscreenalso displays visible output to the user, where the visible output mayinclude a graph, a text, an icon, a video, and so on.

The memory 42 may be configured to store empirical data of a confidencethreshold, which is used by the CPU 43 during processing. The memory 42may be accessed by the CPU 43 and the peripheral interface 46, and thememory 42 may include a high-speed random access memory, and may alsoinclude a non-volatile memory, for example, one or more magnetic diskstorage devices and flash memory devices, or another volatile solidstorage device.

The CPU 43 may be configured to process voice data obtained by the audiocircuit 47 and the microphone 48, and obtain a noise scenario accordingto the voice data; and obtain a confidence threshold according to thenoise scenario and the empirical data of the confidence thresholdpre-stored by the memory 42. The CPU 43 is a control center of themobile phone, connects each part of the entire mobile phone by usingvarious interfaces and lines, and executes various functions of themobile phone and processes data by running or executing softwareprograms and/or modules stored in the memory 42 and invoking data storedin the memory 42, so as to perform entire monitoring on the mobilephone. Optionally, the CPU 43 may include one or more processing units;preferentially, the CPU 43 may integrate an application processor and amodulating and demodulating processor. Optionally, the applicationprocessor mainly processes an operating system, a user interface, theapplication program, and so on, and the modulating and demodulatingprocessor mainly processes wireless communication. It may be understoodthat the modulating and demodulating processor may be not integrated inthe CPU 43. It should be further understood that the foregoing functionis only one of functions that the CPU 43 can execute, and otherfunctions are not limited in the embodiment of the present invention.

The power management chip 44 may be configured to perform power supplyand power management for the CPU 43, the I/O sub-system 49, and thehardware connected to the peripheral interface 46.

The RF circuit 45 is mainly configured to establish communicationbetween the mobile phone and a wireless network (that is, a networkside), so as to implement data obtaining and sending of the mobile phoneand the wireless network, for example, receiving and sending a shortmessage and an e-mail. Specifically, the RF circuit 45 obtains and sendsan RF signal, where the RF signal is also called an electromagneticsignal. The RF circuit 45 converts an electrical signal into anelectromagnetic signal or converts an electromagnetic signal into anelectrical signal, and performs communication with a communicationnetwork and another device through the electromagnetic signal. The RFcircuit 45 may include a known circuit configured to execute thefunctions, where the circuit includes but is not limited to an antennasystem, an RF transceiver, one or more amplifiers, a tuner, one or moreoscillators, a digital signal processor, a CODEC chip-set, a subscriberidentity module (Subscriber Identity Module, SIM), and so on.

The peripheral interface 46, may connect input and output peripherals ofa device to the CPU 43 and the memory 42.

The audio circuit 47 may mainly be configured to obtain audio data fromthe peripheral interface 46, and convert the audio data into theelectrical signal.

The microphone 48 may be configured to obtain the voice data.

The I/O sub-system 49: may control input and output peripheries on thedevice. The I/O sub-system 49 may include a display controller 491 andone or more input controllers 492 configured to control anotherinput/control device. Optionally, one or more input controllers 792obtain the electrical signal from another input/control device or sendthe electrical signal to another input/control device, where the anotherinput/control device may include a physical button (a push button, arocker button, and so on), a dial plate, a slide switch, a joystick, anda clicking wheel. It should be noted that the input controller 492 maybe connected to any one of the following: a keyboard, an infrared port,a USB interface, and an indication device such as a mouse. The displaycontroller 491 in the I/O sub-system 49 obtains the electrical signalfrom the touchscreen 41 or sends the electrical signal to thetouchscreen 41. The touchscreen 41 obtains touch on the touchscreen, thedisplay controller 491 converts the obtained touch into interaction witha user interface object on the touchscreen 41, that is, implementsman-machine interaction, where the user interface object displayed onthe touchscreen 41 may be an icon of running a game, an icon ofconnecting to a corresponding network, a filtering mode, and so on. Itshould be noted that the device may also include an optical mouse, wherethe optical mouse is a touch sensitive surface not displaying visibleoutput, or is an extension of the touch sensitive surface formed by thetouchscreen.

The microphone 48 obtains voice data of a large screen device, and sendsthe voice data to the CPU 43 through the peripheral interface 46 and theaudio circuit 47. The CPU 43 may be configured to process the voicedata, obtain a noise scenario according to the voice data; and obtains aconfidence threshold according to the noise scenario and the empiricaldata of the confidence threshold pre-stored by the memory 42.

The foregoing structure may be configured to execute the method inEmbodiment 1, Embodiment 2, and Embodiment 3. For specific description,reference is made to the description of the method in Embodiment 1,Embodiment 2, and Embodiment 3, which is not repeatedly describedherein.

The technical solution of the present invention provides a mobile phonefor voice identification. In the mobile phone, the noise scenario isobtained, and the confidence threshold is obtained according to thepre-stored empirical data of the confidence threshold and the noisescenario. The mobile phone that flexibly adjusts the confidencethreshold according to the noise scenario greatly improves a voiceidentification rate under a noise environment.

Through the description in the foregoing embodiments, a person skilledin the art may be clearly aware that the embodiments of the presentinvention may be implemented by hardware, or be implemented by firmware,or be implemented by a combination of hardware and firmware. When thepresent invention is implemented by software, the foregoing functionsmay be stored in an apparatus readable medium, or be transmitted as oneor more commands or code on the apparatus readable medium. The apparatusreadable medium includes an apparatus storage medium and a communicationmedium. An optional communication medium includes any medium thatfacilitates transmission of an apparatus program from one place toanother place. The storage medium may be any usable medium that anapparatus can access. The following is taken as an example but is notlimited: The apparatus readable medium may include an RAM, an ROM, anEEPROM, a CD-ROM or another optical disc memory, a disk storage mediumor another disk storage device, or any other medium that can be used tocarry or store an expected program code in a command or data structureform and can be accessed by an apparatus. In addition, any connectionmay appropriately become an apparatus readable medium. For example, ifthe software implements transmission from a website, a server, oranother remote source by using a coaxial cable, an optical cable, atwisted-pair cable, a digital subscriber line (DSL), or a wirelesstechnology, such as infrared, radio, or microwave. Then, the coaxialcable, the optical cable, the twisted-pair cable, the DSL, or thewireless technology, such as infrared, radio, or microwave, is includedin fixation of a home medium. For example, a disk (Disk) and a disc(disc) used in the embodiments of the present invention include acompact disc (CD), a laser disc, an optical disc, a digital versatiledisc (DVD), a floppy disk, and a blue-ray disc. Generally, an optionaldisk magnetically duplicates data, while a disc optically duplicatesdata by using laser. A combination of the foregoing should also fallwithin the protection scope of an apparatus readable medium.

In conclusion, the foregoing description is merely embodiments of thepresent invention, but is not intended to limit the scope of the presentinvention. Any modifications, equivalent replacements, and improvementsmade within the spirit and principle of the present invention shall fallwithin the protection scope of the present invention.

1. A voice identification method, comprising: obtaining voice data;obtaining a confidence value according to the voice data; obtaining anoise scenario according to the voice data; obtaining a confidencethreshold corresponding to the noise scenario; and processing the voicedata, if the confidence value is greater than or equal to the confidencethreshold.
 2. The method according to claim 1, wherein the noisescenario comprises: a noise type; and a noise magnitude.
 3. The methodaccording to claim 2, wherein the obtaining the noise scenario accordingto the voice data specifically comprises: obtaining, according to thevoice data, a frequency cepstrum coefficient of a noise in the voicedata; and obtaining, according to the frequency cepstrum coefficient ofthe noise and a pre-established noise type model, the noise type of thevoice data.
 4. The method according to claim 3, wherein the noise typemodel is established by implementing the following: obtaining noisedata; obtaining a frequency cepstrum coefficient of the noise dataaccording to the noise data; and processing the frequency cepstrumcoefficient according to an Expectation-maximization (EM) algorithm, andestablishing a noise type model.
 5. The method according to claim 3,wherein the noise type model is a Gaussian mixture model.
 6. The methodaccording to claim 2, wherein the obtaining the noise scenario accordingto the voice data specifically comprises: obtaining, according to thevoice data, a feature parameter of the voice data; performing voiceactivity detection according to the feature parameter; and obtaining thenoise magnitude according to a result of the voice activity detection.7. The method according to claim 6, wherein the noise magnitudespecifically comprises: a signal-to-noise ratio; and a noise energylevel.
 8. The method according to claim 1, wherein the obtaining theconfidence threshold corresponding to the noise scenario comprises:obtaining, according to a correspondence between pre-stored empiricaldata of a confidence threshold and the noise scenario, the confidencethreshold corresponding to the noise scenario.
 9. The method accordingto claim 1, further comprising: prompting a user if the confidence valueis smaller than the confidence threshold.
 10. A voice identificationapparatus, comprising: an obtaining unit, configured to obtain voicedata; a confidence value unit, configured to receive the voice dataobtained by the obtaining unit, and obtain a confidence value accordingto the voice data; a noise scenario unit, configured to receive thevoice data obtained by the obtaining unit, and obtain a noise scenarioaccording to the voice data; a confidence threshold unit, configured toreceive the noise scenario of the noise scenario unit, and obtain aconfidence threshold corresponding to the noise scenario; and aprocessing unit, configured to receive the confidence value obtained bythe confidence value unit and the confidence threshold obtained by theconfidence threshold unit, and if the confidence value is greater thanor equal to the confidence threshold, process the voice data.
 11. Theapparatus according to claim 10, further comprising: a modelestablishing unit, configured to obtain noise data, obtain a frequencycepstrum coefficient of the noise data according to the noise data,process the frequency cepstrum coefficient according to anExpectation-maximization (EM) algorithm, and establish a noise typemodel.
 12. The apparatus according to claim 11, wherein the noisescenario unit specifically comprises: a noise type unit, configured toobtain, according to the voice data of the obtaining unit, a frequencycepstrum coefficient of a noise in the voice data, and obtain, accordingto the frequency cepstrum coefficient of the noise and the noise typemodel of the model establishing unit, a noise type of the voice data.13. The apparatus according to claim 10, wherein the noise scenario unitfurther comprises: a noise magnitude unit, configured to obtain,according to the voice data of the obtaining unit, a feature parameterof the voice data, perform voice activity detection according to thefeature parameter, and obtain a noise magnitude according to a result ofthe voice activity detection.
 14. The apparatus according to claim 10,further comprising: a storage unit, configured to store empirical dataof a confidence threshold.
 15. The apparatus according to claim 14,wherein the confidence threshold unit is configured to obtain, accordingto a correspondence between the stored empirical data of the confidencethreshold pre-stored by the storage unit and the noise scenario, theconfidence threshold corresponding to the noise scenario.
 16. A mobileterminal, comprising a processor and a microphone, wherein themicrophone is configured to obtain voice data; and the processor isconfigured to obtain a confidence value and a noise scenario accordingto the voice data, obtain, according to the noise scenario, a confidencethreshold corresponding to the noise scenario, and if the confidencevalue is greater than or equal to the confidence threshold, process thevoice data.
 17. The mobile terminal according to claim 16, furthercomprising a memory, configured to store empirical data of a confidencethreshold.
 18. The mobile terminal according to claim 17, wherein theprocessor is configured to obtain the confidence value and the noisescenario according to the voice data; obtain, according to acorrespondence between the stored empirical data of the confidencethreshold pre-stored by the memory and the noise scenario, theconfidence threshold corresponding to the noise scenario; and processthe voice data if the confidence value is greater than or equal to theconfidence threshold.